Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	144,095
2	Rhode Island	143,086
3	South Dakota	140,247
4	Utah	126,373
5	Tennessee	124,077
6	Arizona	120,672
7	Iowa	117,599
8	Wisconsin	115,690
9	Nebraska	115,382
10	South Carolina	114,953
11	Oklahoma	114,354
12	New Jersey	114,270
13	Arkansas	112,895
14	Delaware	111,456
15	Alabama	110,709
16	Indiana	110,666
17	Illinois	109,085
18	Kansas	108,344
19	Florida	107,838
20	New York	107,760
21	Idaho	107,332
22	Mississippi	106,535
23	Minnesota	106,390
24	Nevada	104,945
25	Montana	104,378
26	Wyoming	103,602
27	Georgia	103,309
28	Kentucky	102,753
29	Massachusetts	102,434
30	Texas	101,707
31	Louisiana	101,072
32	Missouri	99,938
33	Michigan	98,930
34	Connecticut	97,321
35	New Mexico	96,609
36	California	95,694
37	North Carolina	95,360
38	Alaska	95,061
39	Colorado	94,301
40	Ohio	94,068
41	Pennsylvania	93,979
42	West Virginia	89,711
43	Virginia	78,973
44	Maryland	75,933
45	New Hampshire	72,419
46	District of Columbia	69,177
47	Washington	57,013
48	Puerto Rico	53,765
49	Maine	50,062
50	Oregon	47,385
51	Vermont	38,721
52	Hawaii	24,546

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Washington	202
2	West Virginia	193
3	Wyoming	173
4	Colorado	153
5	Louisiana	146
6	Idaho	125
7	Michigan	118
8	North Carolina	115
9	New Mexico	112
10	Rhode Island	101
11	Alaska	97
12	Nevada	97
13	Oregon	92
14	Florida	90
15	Mississippi	88
16	Tennessee	82
17	Texas	81
18	Kentucky	80
19	Montana	79
20	Maine	78
21	North Dakota	77
22	Illinois	76
23	Pennsylvania	76
24	Indiana	74
25	Arizona	73
26	Missouri	72
27	Kansas	68
28	Minnesota	67
29	Ohio	65
30	Utah	62
31	Arkansas	61
32	Connecticut	61
33	South Dakota	60
34	Delaware	57
35	South Carolina	57
36	Oklahoma	56
37	Wisconsin	55
38	Nebraska	53
39	Alabama	48
40	New York	47
41	Georgia	46
42	Iowa	43
43	Massachusetts	42
44	Virginia	41
45	New Hampshire	40
46	District of Columbia	34
47	Puerto Rico	34
48	California	33
49	New Jersey	33
50	Maryland	32
51	Vermont	29
52	Hawaii	23

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,942
2	New York	2,707
3	Massachusetts	2,588
4	Rhode Island	2,556
5	Mississippi	2,453
6	Arizona	2,414
7	Connecticut	2,307
8	Alabama	2,271
9	Louisiana	2,271
10	South Dakota	2,265
11	Pennsylvania	2,123
12	New Mexico	2,029
13	Michigan	2,025
14	North Dakota	2,019
15	Indiana	2,015
16	Illinois	1,977
17	Arkansas	1,930
18	Iowa	1,915
19	Georgia	1,891
20	South Carolina	1,885
21	Oklahoma	1,842
22	Nevada	1,808
23	Tennessee	1,801
24	Texas	1,776
25	Kansas	1,750
26	Florida	1,706
27	Delaware	1,704
28	Ohio	1,689
29	District of Columbia	1,602
30	California	1,594
31	Missouri	1,572
32	West Virginia	1,555
33	Kentucky	1,545
34	Montana	1,503
35	Maryland	1,494
36	Wisconsin	1,333
37	Minnesota	1,327
38	Virginia	1,305
39	North Carolina	1,244
40	Wyoming	1,242
41	Nebraska	1,228
42	Idaho	1,169
43	Colorado	1,159
44	New Hampshire	989
45	Puerto Rico	777
46	Washington	759
47	Utah	714
48	Oregon	632
49	Maine	613
50	Alaska	481
51	Vermont	408
52	Hawaii	348

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Oklahoma	31
2	New Mexico	19
3	Missouri	4
4	Michigan	3
5	Wyoming	3
6	Florida	2
7	Georgia	2
8	Kentucky	2
9	Louisiana	2
10	Mississippi	2
11	New Jersey	2
12	North Carolina	2
13	Pennsylvania	2
14	West Virginia	2
15	Alabama	1
16	Arkansas	1
17	Colorado	1
18	Connecticut	1
19	District of Columbia	1
20	Idaho	1
21	Illinois	1
22	Maine	1
23	Maryland	1
24	Minnesota	1
25	Montana	1
26	Nevada	1
27	New York	1
28	Ohio	1
29	Oregon	1
30	Puerto Rico	1
31	Rhode Island	1
32	South Carolina	1
33	South Dakota	1
34	Tennessee	1
35	Texas	1
36	Virginia	1
37	Washington	1
38	Alaska	0
39	Arizona	0
40	California	0
41	Delaware	0
42	Hawaii	0
43	Indiana	0
44	Iowa	0
45	Kansas	0
46	Massachusetts	0
47	Nebraska	0
48	New Hampshire	0
49	North Dakota	0
50	Utah	0
51	Vermont	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	385,349	1	99
Crowley	Colorado	363,471	2	99
Bent	Colorado	276,134	3	99
Dewey	South Dakota	250,170	4	99
Lincoln	Arkansas	246,161	5	99
Davidson	Tennessee	142,923	191	93
Richland	South Carolina	113,236	981	68
York	South Carolina	112,948	995	68
Orange	California	85,577	2252	28
Pierce	Washington	60,071	2840	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Jerauld	South Dakota	7,948	3	99
Hancock	Georgia	7,922	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,592	1842	41
York	South Carolina	1,370	2092	33
Richland	South Carolina	1,369	2094	33
Davidson	Tennessee	1,366	2098	33
Pierce	Washington	758	2738	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.